






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	42
				a__       							=	42







				def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								super().__init__()
								self.register_modules(unet=__lowerCAmelCase							,       scheduler=__lowerCAmelCase  )







				@torch.no_grad()
				def __call__(    self							,       __lowerCAmelCase = 1							,       __lowerCAmelCase = 5_0							,       __lowerCAmelCase = None							,       __lowerCAmelCase = "pil"							,       __lowerCAmelCase = True							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.unet.config.sample_size
								__magic_name__		:Tuple   =   (batch_size, 3, img_size, img_size)

								__magic_name__		:str   =   self.unet

								# sample x_0 ~ N(0, sigma_0^2 * I)
								__magic_name__		:Optional[int]   =   randn_tensor(__lowerCAmelCase							,       generator=__lowerCAmelCase							,       device=self.device  ) * self.scheduler.init_noise_sigma

								self.scheduler.set_timesteps(__lowerCAmelCase  )

								for t in self.progress_bar(self.scheduler.timesteps  ):
												# here sigma_t == t_i from the paper
												__magic_name__		:Tuple   =   self.scheduler.schedule[t]
												__magic_name__		:int   =   self.scheduler.schedule[t - 1] if t > 0 else 0

												# 1. Select temporarily increased noise level sigma_hat
												# 2. Add new noise to move from sample_i to sample_hat
												__magic_name__  ,					__magic_name__		:Dict   =   self.scheduler.add_noise_to_input(__lowerCAmelCase							,       __lowerCAmelCase							,       generator=__lowerCAmelCase  )

												# 3. Predict the noise residual given the noise magnitude `sigma_hat`
												# The model inputs and output are adjusted by following eq. (213) in [1].
												__magic_name__		:int   =   (sigma_hat / 2) * model((sample_hat + 1) / 2							,       sigma_hat / 2  ).sample

												# 4. Evaluate dx/dt at sigma_hat
												# 5. Take Euler step from sigma to sigma_prev
												__magic_name__		:List[str]   =   self.scheduler.step(__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  )

												if sigma_prev != 0:
																# 6. Apply 2nd order correction
																# The model inputs and output are adjusted by following eq. (213) in [1].
																__magic_name__		:List[Any]   =   (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2							,       sigma_prev / 2  ).sample
																__magic_name__		:str   =   self.scheduler.step_correct(
																    __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       step_output.prev_sample							,       step_output['''derivative''']							,       )
												__magic_name__		:Dict   =   step_output.prev_sample

								__magic_name__		:Dict   =   (sample / 2 + 0.5).clamp(0							,       1  )
								__magic_name__		:str   =   sample.cpu().permute(0							,       2							,       3							,       1  ).numpy()
								if output_type == "pil":
												__magic_name__		:List[Any]   =   self.numpy_to_pil(__lowerCAmelCase  )

								if not return_dict:
												return (image,)

								return ImagePipelineOutput(images=__lowerCAmelCase  )









from __future__ import annotations


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			if len(a__) == 0:
						return []
			a_							,					a_						:						List[Any] 							= min(a__), max(a__)
			a_						:						Tuple 							= int(max_value - min_value) + 1
			a_						:						list[list] 							= [[] for _ in range(a__)]

			for i in my_list:
						buckets[int(i - min_value)].append(a__)

			return [v for bucket in buckets for v in sorted(a__)]


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
	assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]


